What is the term used to describe the measure of the center of a distribution of data?
- Central Distribution
- Central Pattern
- Central Position
- Central Tendency
The term "Central Tendency" is used to describe the measure of the center of a distribution of data. Measures of central tendency aim to describe the central position of a distribution for a data set where the most typical values lie.
______' in the EDA process typically involves cleaning the data and dealing with missing values and outliers.
- communicating
- concluding
- questioning
- wrangling
'Wrangling' in the EDA process typically involves cleaning the data and dealing with missing values and outliers. This step is crucial for preparing the data for subsequent exploration and analysis.
In the presence of outliers, the ________ correlation coefficient can provide misleading results.
- Covariance
- Kendall's Tau
- Pearson's
- Spearman's
In the presence of outliers, the Pearson's correlation coefficient can provide misleading results. Pearson's correlation is sensitive to outliers and hence can be significantly affected by them.
Imagine you're dealing with a classification model. The dataset has a significant amount of missing data that was replaced with the mean. How could this decision have impacted the model's performance?
- It could distort the feature's statistical properties.
- It could increase the model's accuracy.
- It could lead to overfitting.
- It could lead to underfitting.
Replacing missing data with the mean can distort the feature's statistical properties (like variance), which could affect the model's learning and prediction capability.
A data point that lies outside the overall distribution of the dataset is called a(n) _______.
- Anomaly
- Error
- Inlier
- Outlier
A data point that lies outside the overall distribution of the dataset is called an outlier. These are unusual observations that differ significantly from the other data points.
What does the term "Multicollinearity" refer to in the context of Exploratory Data Analysis?
- A condition where the independent variables in a regression model are highly correlated
- A statistical method to determine the correlation between variables
- Correlation among three or more variables
- Correlation between two variables
Multicollinearity refers to a situation where two or more independent variables in a multiple regression model are highly correlated. If these variables are closely correlated, it can be hard for the model to determine the effect of each variable independently, which may lead to unstable estimates.
What factors should be considered when assessing the aesthetics of a data visualization?
- The balance, simplicity, clarity, and color scheme
- The designer's personal taste
- The latest trends in data visualization
- The time it took to create the visualization
Aesthetics in data visualization involve multiple factors including balance (equal weightage to all parts), simplicity (avoiding unnecessary complexity), clarity (clearly understandable), and the color scheme (which can direct attention, represent categories, or express quantities). Good aesthetics make the data easy to understand and the message memorable.
Which method of data imputation is generally most appropriate for MCAR data?
- Mean/Median imputation
- Prediction model
- Random Sample Imputation
- nan
For MCAR data, Random Sample Imputation is a good choice as it assumes that the data are missing completely at random. It works by taking random observations from the dataset and using these to replace the missing values.
When the data is skewed to the right, the _____ will usually be greater than the median.
- Mean
- Median
- Mode
- Range
When data is skewed to the right, it means there are a number of observations with large values, which pull the "Mean" up, making it greater than the median.
Given that you need to create a publication-quality figure, which Python library provides the best control over every aspect of the figure properties?
- Bokeh
- Matplotlib
- Plotly
- Seaborn
Matplotlib provides a low-level, object-oriented API for embedding plots into applications and gives the most control over every aspect of the figure properties. This makes it suitable for creating publication-quality figures.